1. Field of the Invention
The present invention generally relates to the field of optical surface analyzers and more specifically to the characterization and mapping of micropipes (screw dislocations) on grown wafers.
2. Description of Background Art
Silicon carbide (SiC) wafers are used in a variety of industries such as power devices for automobiles and high frequency communication devices. During the production of these wafers, the crystalline structure is subjected to various internal and external stresses that can cause the growth of defects, or dislocations, within the atomic lattice. A screw dislocation is a common dislocation that transforms successive atomic planes within a crystalline lattice into the shape of a helix. Once a screw dislocation propagates through the bulk of a sample during the wafer growth process, a micropipe is formed. The presence of a high density of micropipes within a wafer will result in a loss of yield in the device manufacturing process. Therefore, identifying, classifying, and counting micropipes in a systematic way will help control the SiC wafer manufacturing process and improve device yield.
Current techniques for detecting, counting, and mapping dislocations and micropipes for thin film manufacture rely upon a manual process that involves etching the thin film wafer with a potassium hydroxide (KOH) solution and then retrieving micropipe characterization data through an optical reflection microscope. This process is both destructive to the sample, due to the harsh nature of the KOH etch, and is time consuming, because of the large amount of data that is generated for accurate inspection of the micropipe sites. Etching micropipes with a KOH solution widens the opening of the micropipe, which does allow for easier inspection; however, the wafer is destroyed in the process, which in turn, drives up the cost of manufacture. Also, automated inspection of the micropipe sites has been confined to a manual process where a series of JPEG or BITMAP images of the wafer obtained via a cross-polarization technique are stitched together and analyzed for micropipes. Again, the current techniques are both destructive to the sample and inspect the samples less efficiently, thus translating to a high cost for manufacture.
Conventional optical surface analysis techniques have been used for analysis of transparent wafers; however, significant shortcomings exist. Conventional surface analysis systems are not provided with automated capabilities to collect surface data and cannot avoid contamination of the reflected signals from the top surface of a transparent wafer by reflected light from the back side of a transparent wafer.
What is needed is an automated method for characterizing micropipes contained within silicon wafers, and transparent wafers that: a) detects micropipes; b) classifies micropipes; and c) counts micropipes; through the use of a data processing algorithm that incorporates information regarding: (1) defect size; (2) pit signature; (3) area comparison with specular and scatter images; and (4) tail detection in the specular image.